The scaling of genetic diversity in a changing and...

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The scaling of genetic diversity in a changing and fragmented world

Miguel Arenas1,2,*, Stefano Mona1,2, Audrey Trochet3, Anna Sramkova Hanulova1,2,

Mathias Currat4, Nicolas Ray5,6, Lounes Chikhi7,8,9, Rita Rasteiro7,10, Dirk S.

Schmeller3,11 and Laurent Excoffier1,2

1 Computational and Molecular Population Genetics Lab (CMPG), Institute of Ecology and Evolution, University of Bern, Baltzerstrasse 6, 3012 Berne, Switzerland. 2 Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland. 3 Station d'Ecologie Expérimentale du CNRS à Moulis, USR 2936, 09200 Saint Girons, France. 4 Anthropology, Genetics and Peopling History Lab, Anthropology Unit – Department of Genetics & Evolution, University of Geneva, Geneva, Switzerland. 5 EnviroSPACE Laboratory, Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland. 6 Forel Institute, University of Geneva, Geneva, Switzerland. 7 Instituto Gulbenkian de Ciências, Oeiras, Portugal 8 CNRS, Université Paul Sabatier, ENFA, UMR 5174 EDB (Laboratoire Evolution et Diversité Biologique), Toulouse, France 9 Université de Toulouse, UMR 5174 EDB, Toulouse, France 10 University of Leicester, University Road, Leicester, LE1 7RH, United Kingdom 11 Helmholtz Center for Environmental Research, Department of Conservation Biology, Permoserstrasse 15, 04318 Leipzig, Germany. * Corresponding author Current Email addresses:

Miguel Arenas: [email protected] Stefano Mona: [email protected] Audrey Trochet: [email protected] Anna Sramkova Hanulova: [email protected] Mathias Currat: [email protected] Nicolas Ray: [email protected] Lounes Chikhi: [email protected] Rita Rasteiro: [email protected] Dirk S. Schmeller: [email protected] Laurent Excoffier: [email protected]

Chapter for the SCALES book.

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Species living in a changing world Most species do not live in a constant environment over space or time. Their

environment is often heterogeneous with a huge variability in resource availability and

exposure to pathogens or predators, which may affect the local densities of the species.

Moreover, the habitat might be fragmented, preventing free and isotropic migrations

between local sub-populations (demes) of a species, making some demes more isolated

than others. For example, during the last ice age populations of many species have

migrated towards refuge areas from which re-colonization originated when conditions

improved. However, population extinctions may have occurred for populations that

could not move fast enough or could not adapt to the new environmental conditions.

Populations living in these types of dynamic environments are often referred to and

modeled as metapopulations. Several studies have focused on the description of their

demography, they probability of extinction and expected patterns of diversity at

different scales. Importantly, all these evolutionary processes may affect genetic

diversity, which can affect the chance of populations to persist. In this chapter we

overview the consequences of fragmentation, long-distance dispersal, range

contractions and range shifts on genetic diversity. In addition, we describe new

methodologies to detect and quantify underlying evolutionary processes from sampled

genetic data.

1. Spatial and temporal genetic simulation using SPLATCHE2 Computer simulations mimic the processes that occur in the real world and allow us to

study which patterns may affect systems. We have developed the program

SPLATCHE2 (http://www.splatche.com) (Ray et al. 2010), which performs spatially

explicit simulations of genetic data under environmental variable scenarios and

accounting for recombination, complex migration and long-distance dispersal. As input,

the program requires a map (specified by a grid of demes) where the carrying capacity

(K) and the migration rate must be user-specified for each deme. Optionally, both K and

migration rate can change with time (moreover, anisotropic migration is also

implemented). Other important inputs are related with demography (e.g., initial

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population size and geographic origin, growth rate, generation time, total number of

generations and a number of demographic models). Then, SPLATCHE2 performs a

demographic simulation over the map followed by a coalescent simulation based on

user-defined samples (Figure 1). The coalescent simulation just traces the evolutionary

history of the sampled genes going backwards in time until their most recent common

ancestor. It is followed by a simulation of genetic data (DNA, STRs and SNPs) along

the coalescent (gene) genealogy. Although the model makes several assumptions (like a

molecular clock or non-overlapping generations) it is probably one of the most realistic

software available and has been used in a variety of important publications. Below we

describe some important applications of this program for scaling genetic diversity under

complex evolutionary scenarios.

2. Influence of habitat fragmentation on genetic diversity Previous studies have suggested that environmental heterogeneity can affect genetic

diversity, but these effects were not evaluated at different spatial scales. By using the

results from extensive simulations, we address here the influence of fragmented habitats

at different scales on the species genetic diversity. Using SPLATCHE2, we simulated

range expansions where demes were partitioned into groups (patches) by adding barriers

to dispersal. We also included scenarios with long-distance dispersal events, where

individuals can migrate to non-neighboring demes. Then, samples were collected within

demes, patches, regions and at the global landscape level.

As expected, we found that strong levels of fragmentation result in a severe loss of

genetic diversity in the population at a global scale, but we also found that the detection

of this decreased diversity requires sampling at different scales. Moreover, we varied

fragmentation intensity at specific time points and we found that local genetic diversity

and population differentiation were markedly affected by ancient fragmentation, and

much less by recent events. Our results explain why recent habitat fragmentation does

not always lead to detectable signatures in the genetic structure of populations.

Conversely, if habitat fragmentation is removed, it also takes a long time to recover lost

diversity by natural processes, suggesting that long-term conservation measures (e.g.,

by restoring gene flow) should be implemented to locally restore previously lost genetic

diversity. We also found that species with long-distance dispersal abilities can however

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migrate out of the barriers. As a consequence, their diversity is less influenced by the

fragmented landscape.

3. Influence of range contractions and range shifts on genetic diversity Range contractions and range shifts may occur as a consequence of temporal climatic

fluctuations, depending on the geographical structure of the landscape, the duration of

the climatic changes, or the species dispersal abilities. Under such environmental

changes, a common response of species is migration towards more suitable regions.

Many studies have analyzed the migration behaviour and spatial distribution of range-

contraction and -shifting species, nevertheless less attention has been paid to the

influence of such processes on genetic diversity. By using SPLATCHE2, we simulated

DNA sequence data in populations suffering diverse range shifts and contractions over a

landscape constituted by a grid of demes (Arenas et al. 2012). Simulated scenarios of

range shifts and range contractions varied according to dispersal abilities and migration

patterns. For example fast range contractions (e.g., as a consequence of a fast climate

change) may lead to the extinction of populations that do not move. We analyzed

genetic diversity of the simulated data. Contrary to our expectations, we found that fast

contractions had less effect on genetic diversity. Fast contractions preserve higher levels

of diversity and induced lower levels of genetic differentiation among refuge areas than

slow contractions towards refuge areas. Thus slow contractions have the highest

negative impact on final (low) levels of diversity. Contrastingly, fast range shifts lead to

lower levels of diversity than slow range shifts. Interestingly, we found that species

actively migrating towards refuge areas can actually bring additional diversity to these

areas, but only if the range contraction is rapid. When contractions or shifts are slow,

we found that active migrations towards refuge areas could lead to a more pronounced

loss of diversity than if migration was isotropic (Arenas et al. 2012). These results

suggest that species with different generation times and different migration abilities

should be differently affected by environment change.

BOX 1. Effect of range contractions on principal component analysis of European

molecular diversity

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The genetic signal of range contractions (RCs) can be also observed in genetic gradients

estimated by principal component analysis (PCA). Initial studies that represented

genetic relationships among human populations with PCA revealed the presence of a

southeast–northwest (SE-NW) gradient of genetic variation in current European

populations, which was interpreted as the result of a diffusion process of early Neolithic

farmers during their expansion from the Middle East. However, this interpretation has

been largely questioned, as PCA gradients may occur even when there is no expansion,

and because the first PC axis is often orthogonal to the expansion axis. However, the

effect of more complex evolutionary scenarios on PCA, such as those including both

range expansions and contractions, had not been investigated.

In a recent study, we (Arenas et al. 2013) have performed simulations of range

contractions that might have occurred during the last glacial maximum period to better

understand the formation of genetic gradients across Europe. In particular, we have

simulated range contractions of human Paleolithic populations and admixture between

Paleolithic and Neolithic populations over Europe (see Figure 1). The simulations were

performed for diverse levels of admixture and under two range contraction scenarios

where the refuge areas were either over all southern Europe or only in the Iberian

Peninsula (see Figure 1). We observed that the first PC (PC1) gradients were orthogonal

to the expansion axis, but only when the expansion was recent (Neolithic). More ancient

(Paleolithic) expansions altered the orientation of the PC1 gradient due to 1) a spatial

homogenization of genetic diversity over time, and 2) the exact location of the LGM

refugia. Overall we found that PC1 gradients consistently follow a SE-NW orientation

if there is a large Paleolithic contribution to the current European gene pool, and if the

main refuge area during the last ice age was in the Iberian Peninsula. Our study suggests

that the observation of a SE-NW PC1 gradient is compatible with the view that range

contractions have affected observed patterns of genetic diversity, and suggest that the

genetic contribution of Neolithic populations to the current European gene pool may

have been limited (Figure 2). Although this study was focused on humans, this

framework could be applied to other species that might have experimented range

contractions as a consequence of environmental changes.

END BOX 1.

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4. Inference of fragmentation levels from genetic data gathered at different scales over the species range Populations living in a heterogeneous environment usually show a large variance in

local population densities and migration rates, and generally present less local genetic

diversity and higher levels of population differentiation than populations of similar size

living in a constant and uniform environment. This is because genetic diversity is more

rapidly lost in small demes than it is gained in large demes, leading to higher rates of

local genetic drift.

Patterns of genetic diversity have been used to assess many properties of a population,

but no attempt has been made to directly estimate the degree of environmental

heterogeneity directly from patterns of diversity at different scales. It would therefore be

useful to be able to infer the degree of environmental heterogeneity directly from

genetic data, especially for sparse and cryptic species, or for species for which the exact

definition of the population is difficult to assess.

We have simulated environmental heterogeneity using SPLATCHE2 where local deme

carrying capacities (K) are drawn from a Gamma distribution with mean K and shape

parameter α. Note that small values of α (typically α < 1) are indicative of strong

environmental heterogeneity, where a few demes have very high population densities

and most others have very low densities (even being zero, which correspond to

uninhabitable regions). Therefore, because habitat fragmentation usually creates

uninhabitable regions, it is also associated to high levels of environmental

heterogeneity. On the other hand, large values of α (typically α >5) imply little

environmental heterogeneity, such that most demes have a very similar carrying

capacity. Previous studies have shown that both local genetic diversity and levels of

population differentiation would strongly depend on α, suggesting that patterns of

genetic diversity at different scales could be used to infer α, and therefore, indirectly,

the level of environmental heterogeneity.

We used an Approximate Bayesian Computation framework to infer the shape

parameter of a Gamma distribution directly from patterns of genetic diversity of several

samples taken from a population having gone through a recent range expansion. Our

results show that the degree of environmental heterogeneity (α) can be very well

estimated if all other parameters of the model are known (Figures 3). When all other

parameters need to be co-estimated, the estimation of α becomes difficult, and we can

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mainly distinguish small from large α values (Figure 4). In other words, we have only

power to distinguish very heterogeneous environments from more homogeneous ones,

but little prospect to get accurate estimations of α.

BOX 2. Sex-biased dispersal

Population genetic structure is influenced by migration patterns. This includes sex-

biased dispersal, which is frequent in many species, likely impacting life-history

evolution, population genetic structure and metapopulation functioning. In population

genetics, sex-biased dispersal may not only reflect a difference in the number of

dispersing individuals of one sex in relation to the opposite sex, but also the unequal

reproductive success of dispersers. Fine-scale genetic structure and adaptation to local

environments might therefore be promoted by sex-biased dispersal. Sex-biased dispersal

can be identified and quantified to some extent by comparing the genetic differentiation

of females to that of males, and by looking at sex-related genetic systems. The sex with

the highest dispersal frequency would have a lower genetic differentiation among

different subpopulations (i.e. as measured by the genetic parameter FST). Similarly, sex-

biased dispersal could be measured by comparing the level of genetic structure inferred

from nuclear markers (inherited by both parents) to that indicated by mtDNA (which is

transmitted only by females) or Y chromosome (transmitted only by males). If the level

of genetic differentiation inferred from mtDNA is higher than that inferred from nuclear

markers, male-biased dispersal may be assumed. Simulations, undertaken with a

different program inspired by SPLATCHE2 (Rasteiro et al. 2012), clearly show that

different patterns of genetic differentiation can be detected under three scenarios, 1)

bilocality (no sex-biased dispersal), 2) matrilocality (male-biased dispersal), and 3)

patrilocality (female-biased dispersal, Figure 5). Y-chromosome genetic diversity is

very low, especially in the patrilocality scenario for which only one Y-haplotype often

remain after 1000 simulated generations. Note that the same effect was not seen in

simulated mtDNA, probably due to differences in mutation rates and types of markers

(Rasteiro et al. 2012). Indeed, the authors showed that the simple difference in mutation

rates between the two types of sex-related genetic systems is sufficient to create an

asymmetry that could be mistaken for differences in migration rates, even under

bilocality scenarios.

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Accounting for sex-biased migration in population and conservation genetics studies is

of great importance as significant differences in sex-biased dispersal have been

demonstrated among different taxonomic groups. Dispersal of mammals, reptiles and

fishes were more frequently male-biased whereas dispersal in birds was more frequently

female-biased (Figure 6). Therefore, knowledge on sex-biased dispersal may prove

essential to develop and assess habitat management and landscape planning strategies

for different species.

In many species population decline has been linked directly to loss and fragmentation of

habitats and indirectly to reduced inter-patch dispersal. Concerns about habitat

fragmentation and landscape structure are usually based on the ability of wildlife to

disperse between the blocks of habitat types that they require. Our simulations showed

that patterns of sex-biased dispersal can have important consequences on some genetic

markers and conversely they should inform us on the importance of sex-biased dispersal

in natural systems that are difficult to study. Some studies have suggested that the

different sexes may have a differing impact on demographic connectivity at different

scales, the less dispersing sex more on local scales, while the more and farther

dispersing sex on larger scales. Another consequence of sex-biased dispersal is that the

rate of natural recolonization of locally extinct populations may be slower as it requires

that both sexes disperse. Sex-biased dispersal may also act as a buffer against reduction

of genetic variability due to high genetic drift in populations with small effective size

(Schmeller and Merila 2007). Ultimately, explorations of the implication of unequal

effective population size, migration rate and non-random individual dispersal will be

necessary for synthesizing ecological and genetic theory on dispersal and population

structure.

END OF BOX 2

6. Concluding remarks In this chapter we described the strong influences that habitat fragmentation and

dispersal heterogeneity can have on genetic diversity, at different geographical and

temporal scales. To this purpose, we mainly used the SPLATCHE framework to

perform spatially explicit simulations of genetic diversity under complex demographic

models, also allowing for temporal heterogeneity. We found that fragmented habitats

often have a significant loss of genetic diversity relative to homogeneous habitats. This

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effect was reduced in species with long distance dispersal abilities. Similarly, range

contractions led to a loss of genetic diversity, in particular when the contraction was

slow. As a consequence, the generation time of species needs to be taken into account

when considering genetic diversity after climatic changes. Species with shorter

generation times should suffer from more diversity loss after a range contraction than

long lived species (Arenas et al. 2012). We note however, that such species may also

adapt faster to new environments. Fast range shifts, on the contrary, reduced genetic

diversity more than slow range shifts where more individuals can track favorable

environments. Indeed species with low migration rates and going through fast range

shifts can easily become extinct (Arenas et al. 2012). In addition, we found signatures of

range contractions on diversity by using PCA. In this case, a re-expansion after a range

contraction introduces spatial genetic diversity gradients that depend on the location of

refuge areas (Arenas et al. 2013). We also described a procedure to detect the level of

habitat fragmentation from observed patterns of genetic diversity. Finally, we

performed simulations incorporating sex-biased migration and found that such a bias

could highly impact genetic data, which can therefore be used to infer sex-biased

dispersal in species that are difficult to study in the field. The fact that habitat

fragmentation, dispersal patterns, and range movements strongly alter genetic diversity

of species implies that they need to be considered for biodiversity conservation

strategies.

Acknowledgements This work was supported by a EU/FP7 grant N° 226852 (SCALES). LC was supported

by the Laboratoire d’Excellence (LABEX) entitled TULIP (ANR-10-LABX-41) and by

the FCT project ref. PTDC/BIA-BEC/100176/2008.

References ARENAS M, FRANCOIS O, CURRAT M, RAY N, EXCOFFIER L (2013) Influence of

admixture and paleolithic range contractions on current European diversity gradients. Mol. Biol. Evol. 30: 57-61.

ARENAS M, RAY N, CURRAT M, EXCOFFIER L (2012) Consequences of Range Contractions and Range Shifts on Molecular Diversity. Mol. Biol. Evol. 29: 207-218.

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PIAZZA A, RENDINE S, MINCH E, MENOZZI P, MOUNTAIN J et al. (1995) Genetics and the origin of European languages. Proc Natl Acad Sci U S A 92: 5836-5840.

RASTEIRO R, BOUTTIER PA, SOUSA VC, CHIKHI L (2012) Investigating sex-biased migration during the Neolithic transition in Europe, using an explicit spatial simulation framework. Proc Biol Sci 279: 2409-2416.

RAY N, CURRAT M, FOLL M, EXCOFFIER L (2010) SPLATCHE2: a spatially explicit simulation framework for complex demography, genetic admixture and recombination. Bioinformatics 26: 2993-2994.

SCHMELLER DS, MERILA J (2007) Demographic and genetic estimates of effective population and breeding size in the amphibian Rana temporaria. Conserv. Biol. 21: 142-151.

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Figures

Figure 1. Timeline simulation of complex scenarios of range expansion, range

contraction and posterior re-expansion. Each plot corresponds to a snapshot of the

program SPLATCHE2. White areas indicate unoccupied demes while green areas

represent occupied demes. Snapshots presented at each line differ in 50 generations, see

detailed settings in (Arenas et al. 2013). At the top, we describe a range expansion over

Europe from the Near East. Then, we show a range contraction from the north to the

south, which mimic the LGM period and leads to two refuge areas (all south of Europe

(A) and only the Iberian Peninsula (B)). At the bottom, we show a re-expansion from

both refuge areas.

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Figure 2. Influence of range contraction on PCA maps. SNP data PC maps for

admixture of Neolithic (20%) and Paleolithic (80%) range expansions from Middle

East. (A) Illustrative example of PCA derived from a range expansion. The PC1

gradient has a SW-NE orientation. (B) Illustrative example of PCA derived from range

expansion followed by a RC towards all south of Europe, and posterior re-expansion.

The PC1 gradient has an E-W orientation. (C) Illustrative example of PCA derived from

range expansion followed by a RC towards only the Iberian Peninsula, and posterior re-

expansion. The PC1 gradient has an NW-SE orientation. (D) Original PC1 map inferred

from Piazza et al. (1995) [© 1995 National Academy of Sciences, USA] with a

superimposed line connecting positive and negative PC1 centroids. The PC1 gradient

shown in (C), which is the most similar to such finding from real data (D), was also

found in scenarios with larger Paleolithic contribution and either pure range expansions

or range expansions with RC towards the Iberian Peninsula (further details in Arenas et

al. 2013).

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Figure 3. ABC estimation of α from genetic diversity simulated in species with small

and large carrying capacity average when all other parameters of the model are known.

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Figure 4. Optimal distinction between small and large α values when all parameters of

the range expansion model need to be co-estimated with the environmental

heterogeneity. The plot shows the estimated proportion of times where α was

incorrectly estimated as below or above a threshold (a given true value). This incorrect

assignment is minimized for α=0.63 (blue line), showing a maximal power to

distinguish between values of α above and below this value. Here, the misclassification

rate is inferred from an analysis of the plot of true (x-axis) vs. estimated (y-axis) α

values shown in the central insert. Misclassification rate is obtained as the sum of the

proportion of points in the blue regions relative to those in the orange regions on the left

and right hand side of the blue line.

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Figure 5: Genetic differentiation patterns under sex-biased migration patterns. Simulations were performed using a forward simulation program

similar to SPLATCHE2. A square environment of 400 demes (20x20) was simulated under three scenarios, 1) bilocality (no sex-biased

dispersal), 2) matrilocality (male-biased dispersal), and 3) patrilocality (female-biased dispersal). For each scenario we simulated independent

autosomal loci, Y and X chromosome and mtDNA sequences. For each scenario and genetic marker type we computed a measure of genetic

differentiation between demes at increasing distances. For simplicity only demes from the diagonal were used and compared to the same deme

located in one of the corners (deme 19,19). As the panels show, sex-biased migration has a strong impact on the overall level of genetic

differentiation, and on the differences between markers. The results also show that mtDNA and Y chromosome markers do not necessarily play

symmetrical roles in the patrilocality and matrilocality scenarios because they differ also in mutations rates, as noticed by Rasteiro et al. (2012).

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Figure 6. Phylogenetic tree of the ancestral character states reconstruction of sex-biased

dispersal based on a parsimonious method on the 216 species (275 populations from

publications) used. Branches and tips are coloured in blue for a male biased dispersal

state and in red for a female biased dispersal state. In grey, branches for which the

reconstruction method did not allow one to choose between a male or a female bias.

Numbers on nodes correspond to: 1. Bilateria, 2. Arthropoda, 3. Osteichthyes, 4. Fishes,

5. Tetrapoda, 6. Mammals, 7. Amniota, 8. Sauria, 9. Neognathae, 10. Neonaves, 11.

Birds, 12. Batrachia.

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